System for optimizing oxygen in a boiler

ABSTRACT

A method and apparatus for optimizing air flow to a boiler of a power generating unit using advanced optimization, modeling, and control techniques. Air flow is optimized to maintain flame stability, minimize air pollution emissions, and improve efficiency.

RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No. 11/380,084(filed Apr. 25, 2006) which is fully incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to the operation of a powergenerating unit, and more particularly to a method and apparatus foroptimizing the air flow to a boiler of a power generating unit usingadvanced optimization, modeling, and control techniques to maintainflame stability, minimize air pollution emissions, and improveefficiency.

BACKGROUND OF THE INVENTION

In a conventional fossil fuel-fired (e.g., coal-fired) power generatingunit, a fossil fuel/air mixture is ignited in a boiler. Large volumes ofwater are pumped through tubes inside the boiler, and the intense heatfrom the burning fuel turns the water in the boiler tubes intohigh-pressure steam. In an electric power generating application, thehigh-pressure steam from the boiler passes into a turbine comprised of aplurality of turbine blades. Once the steam hits the turbine blades, itcauses the turbine to spin rapidly. The spinning turbine causes a shaftto turn inside a generator, creating an electric potential.

As used herein, the term “power generating plant” refers to one or morepower generating units. Each power generating unit drives one or moreturbines used for generating electricity. A power generating unit istypically powered by fossil fuels (including but not limited to, coal,natural gas or oil), and includes a boiler for producing hightemperature steam; air pollution control (APC) devices for removal ofpollutants from flue gas; a stack for release of flue gas; and a watercooling system for condensing the high temperature steam. A typicalpower generating unit will be described in detail below.

Boiler combustion or other characteristics of a fossil fuel-fired powergenerating unit are influenced by dynamically varying parameters of thepower generating unit, including, but not limited to, air-to-fuelratios, operating conditions, boiler configuration, slag/soot deposits,load profile, fuel quality and ambient conditions. Changes to thebusiness and regulatory environments have increased the importance ofdynamic factors such as fuel variations, performance criteria, emissionscontrol, operating flexibility and market driven objectives (e.g., fuelprices, cost of emissions credits, cost of electricity, etc.).

Over the past decade, combustion optimization systems have beenimplemented for advanced control of the combustion process within thefurnace. Typically, combustion optimization systems interface with adistributed control system (DCS) of a power generating unit. Based uponthe current operating conditions of the power generating unit, as wellas a set of operator specified goals and constraints, the combustionoptimization system is used to compute the optimal fuel-to-air stagingwithin the furnace to achieve the desire goals and constraints.

Combustion optimization systems were originally implemented to reducenitrogen oxides (NOx) produced in the furnace and emitted to theatmosphere via the stack. U.S. Pat. No. 5,280,756 to Labbe et al.(issued Jan. 25, 1994) teaches a method and system for controlling andproviding guidance in reducing NOx emissions based upon controllablecombustion parameters and model calculations while maintainingsatisfactory plant performance. U.S. Pat. No. 5,386,373 to Keeler et al.(issued Jan. 31, 1995) teaches the use of a predictive model ofemissions including NOx in conjunction with a control system. U.S. Pat.No. 6,381,504 to Havener et al. (issued Apr. 30, 2002) describes amethod for optimally determining the distribution of air and fuel withina boiler by aggregating the distributions of air and fuel into twocommon variables, performing an optimization, and then computing theoptimal distribution of fuel and air based upon the optimal values ofthe aggregated variables. U.S. Pat. No. 6,712,604 issued to Havlena(issued Mar. 30, 2004) describes a system for controlling the combustionof fuel and air in a boiler such that the distributions of NOx and COare maintained to average less than the maximum permitted levels.

Recently, combustion optimization approaches have been used to controlboiler parameters in addition to NOx, including unit heat rate, boilerefficiency, and mercury emissions. U.S. patent application Ser. No.10/985,705 (filed Nov. 10, 2004) entitled “System for Optimizing aCombustion Heating Process” (fully incorporated herein by reference)teaches an approach to modeling controllable losses in a powergenerating unit and a method for optimizing the combustion process basedupon these controllable losses. U.S. patent application Ser. No.11/301,034 (filed Dec. 12, 2005) entitled “Model Based Control andEstimation of Mercury Emissions” (fully incorporated herein byreference) teaches a system and method for reducing mercury emissionsfrom a coal-fired power plant while observing limits on the amount ofcarbon in the fly ash produced by the combustion process.

The combustion optimization approaches described above provide highlevel control of the boiler similar to that performed by the operator.The combustion optimization system relies upon the Distributed ComputerSystem (DCS) to execute its commands. For example, the DCS is typicallyused to control the level of oxygen in the furnace, the position of airflow dampers, and the amount of fuel entering the power generating unit.It is assumed that the DCS, typically using a design of cascading andinteracting proportional, integral and derivative (PID) control,provides a sufficiently accurate and fast control of the basic controlloops in the boiler. Thus, the combustion optimization system is used tocompute the optimal setpoints for the boiler and the DCS is used toprovide accurate and fast control of the basic loops to these setpoints.

Although the DCS is typically capable of providing adequate regulatorycontrol of the power generating unit, there are a significant number ofsituations where a DCS based control scheme is not sufficient formaintaining the appropriate operating conditions in the boiler. Forexample, large variations in the heat content of the fuel and inaccurateoxygen sensor readings can lead to flame extinction of burners usingtypical DCS control schemes. If large fuel variations or inaccurateoxygen readings are known to exist, operators are required to eithermanually control the boiler or use artificially high levels of oxygen toprevent flame extinction. Either method leads to sub-optimal performanceof the boiler.

Variations in heat content of the fuel can lead to flame extinction orflame instability of burners due to inadequate air flow. Specifically,as the heat content of the fuel changes, the method in which air isinjected into the boiler varies. Two streams of air (i.e., primary airand secondary air) are used for injecting air into the boiler. (For easeof explanation in this background section, a boiler without overfire airis considered.) The primary air is used to transport coal to theburners. The secondary air is used to provide excess oxygen (i.e.,oxygen introduced into the boiler above that required for fullcombustion of the fuel) and to swirl around the primary air and fuelinside the burner. The flow of secondary air around the primary air iscritical to maintaining proper flame and combustion characteristics inthe boiler. The total amount of both primary air and secondary airinjected into the boiler is determined by load demand and the requiredoxygen at the exit of the furnace.

As the heat content of the fuel varies, the amount of primary air neededto transport the fuel to a burner changes. If the heat content drops,more fuel is needed, and consequently more primary air is needed totransport the fuel. Therefore, a lower rank fuel requires more primaryair than a higher rank fuel. Since the overall amount of total airtypically injected into the boiler stays approximately constant at afixed load, the secondary air that is injected into the burner varies asthe primary air varies to maintain constant total air. As the heatcontent of the fuel decreases, primary air increases resulting in adecrease in secondary air. At some point, if the heat content falls toolow, flame extinction of the burner becomes highly probable due to thelack of sufficient secondary air. Because it is difficult and expensiveto instantaneously determine the heat content of the fuel using sensors,it is difficult to create an algorithm to recognize this situation inthe DCS using commonly available logic and control schemes. Therefore,current DCS logic and control schemes can lead to the high probabilityof flame extinction if the heat content of the fuel is significantlylower than expected.

A second cause of flame instability or extinction is inaccurate oxygenmeasurements in the boiler. In this regard, one or more oxygen sensorsare located in the furnace where combustion is complete to measureoxygen concentration in the flue gas. It should be appreciated that themeasured oxygen concentration is also indicative of “excess oxygen”since a percentage value for excess oxygen can be determined from themeasured oxygen concentration. If the oxygen sensors produce a higherthan actual reading of oxygen concentration (indicative of higher levelsof excess oxygen), the DCS will be forced to lower secondary air to theboiler. Once again, if the secondary air falls too low, flame extinctionbecomes highly probable. Therefore, an artificially high reading ofoxygen from the sensors in the furnace can lead to flame extinctionusing current DCS control schemes.

To avoid these problems, prior to the invention disclosed herein,operators of power generating units have had two options:

-   -   1) Manual Control: The operator directly controls the secondary        air rather than using a PID control loop for maintaining the        level of oxygen in the furnace. Using this approach, the        operator must constantly monitor a variety of boiler operating        conditions such as the excess oxygen in the furnace, the fuel        entering the furnace, and the amount of secondary air. If the        secondary air becomes too low to maintain proper flame        stability, due either to low heat content of the fuel or drift        in the oxygen sensors or a combination of these factors, the        operator must manually increase the amount of secondary air to        prevent flame extinction. (This subsequently increases the        oxygen in the boiler.) This approach requires constant operator        attention to prevent flame extinction. Because it is difficult        to maintain constant attention, many operators choose the second        option which is described hereinafter.    -   2) Artificially High Setpoint for Oxygen: A second approach to        prevent flame extinction, due either to low heat content of the        fuel or drift in the oxygen sensors or a combination of both, is        to use an artificially high setpoint for oxygen in the furnace.        Using this approach, the operator sets the oxygen at a level        such that under any circumstances the secondary air will be        sufficient to prevent flame extinction. Although this approach        is simple and can be implemented by current DCS systems, it has        serious drawbacks. High levels of oxygen in the boiler lead to        formation of high levels of nitrogen oxides (NOx) in the boiler.        Because NOx is a major regulated air pollutant, this approach        leads to unwanted and unnecessary air pollution. In addition, an        increase in the oxygen also leads to a decrease in overall        efficiency of the boiler. Therefore, setting an artificially        high value for oxygen does prevent flame extinction and is        possible using current DCS control schemes; however, it leads to        excess air pollution and a reduction in overall power generating        unit efficiency.

The existing solutions to the foregoing problems are not suitable foruse with a combustion optimization system. In this regard, to properlyimplement a combustion optimization system all critical control loops,such as oxygen, must be under automatic control. Therefore, the firstapproach, which relies on manual control of excess oxygen, cannot beused in conjunction with a combustion optimization system. The secondapproach may be used in conjunction with a combustion optimizationsystem but places an artificially high constraint on the lower level ofoxygen in the boiler. Since most combustion optimization systems areinstalled to improve heat rate or decrease NOx, lowering excess oxygenin the boiler below this artificially high constraint is critical to thesuccess of such systems. Therefore, an alternative approach for controlof excess oxygen is needed. This alternative approach would havesignificant environmental and economic benefits whether it is used inconjunction with a combustion optimization system or used without such asystem.

The present invention provides a system that overcomes theabovementioned drawbacks of the prior art, and provides advantages overprior art approaches to control and optimization of power generatingunits.

SUMMARY OF THE INVENTION

In accordance with the present invention, there is provided a controlsystem for controlling oxygen in flue gas produced by a boiler for afossil fuel fired power generating unit, said control system comprising:(a) means for receiving data signals from a plurality of sensors forsensing parameters associated with operation of the power generatingunit; (b) a model for predicting oxygen in the flue gas produced by theboiler, the model comprising: a plurality of inputs for receiving inputvalues associated with manipulated variables and disturbance variablesassociated with the power generating unit, and one or more outputs forproviding output values associated with controlled variables, at leastone output providing a first output value indicative of predicted oxygenin the flue gas in accordance with the input values; (c) an optimizerfor determining optimal setpoint values for the manipulated variablesusing the model, wherein said optimal setpoint values for themanipulated variables produce optimal air flow into the boiler; and (d)means for controlling operation of the power generating unit using theoptimal setpoint values for the manipulated variables.

In accordance with another aspect of the present invention, there isprovided a method for controlling oxygen in flue gas produced by aboiler for a fossil fuel fired power generating unit, said methodcomprising: (a) receiving data signals from a plurality of sensors forsensing parameters associated with operation of the power generatingunit; (b) using a model to predict oxygen in the flue gas produced bythe boiler, the model comprising: a plurality of inputs for receivinginput values associated with manipulated variables and disturbancevariables associated with the power generating unit, and one or moreoutputs for providing output values associated with controlledvariables, at least one output providing a first output value indicativeof predicted oxygen in the flue gas in accordance with the input values;(c) determining optimal setpoint values for the manipulated variablesusing the model, wherein said optimal setpoint values for themanipulated variables produce optimal air flow into the boiler; and (d)controlling operation of the power generating unit using the optimalsetpoint values for the manipulated variables.

According to still another aspect of the present invention, there isprovided a control system for controlling a parameter associated withoperation of a fossil fuel fired power generating unit, said controlsystem comprising: (a) means for receiving data signals from a pluralityof sensors for sensing the parameter associated with operation of thepower generating unit, said plurality of sensors located in differentregions of the power generating unit; (b) an optimization systemcomprising: (1) a model for predicting the parameter sensed by theplurality of sensors, the model comprising: a plurality of inputs forreceiving input values associated with manipulated variables anddisturbance variables associated with the power generating unit, and aplurality of outputs for providing a plurality of output valuesassociated with controlled variables, said plurality of output valuesindicative of predicted values for the parameter sensed by the pluralityof sensors, wherein each of said plurality of output values isrespectively associated with each of said plurality of sensors; (2) anoptimizer for determining optimal setpoint values for the manipulatedvariables using the model, said optimal setpoint values determined bythe optimizer in accordance with at least one goal and at least oneconstraint associated with operation of the power generating unit; and(c) means for controlling operation of the power generating unit usingthe optimal setpoint values for the manipulated variables.

According to yet another aspect of the present invention, there isprovided a method for controlling a parameter associated with operationof a fossil fuel fired power generating unit, said method comprising:(a) receiving data signals from a plurality of sensors for sensing theparameter associated with operation of the power generating unit, saidplurality of sensors located in different regions of the powergenerating unit; (b) optimizing operation of the fossil fuel fired powergenerating unit using an optimization system comprising: (1) a model forpredicting the parameter sensed by the plurality of sensors, the modelcomprising: a plurality of inputs for receiving input values associatedwith manipulated variables and disturbance variables associated with thepower generating unit, and a plurality of outputs for providing aplurality of output values associated with controlled variables, saidplurality of output values indicative of predicted values for theparameter sensed by the plurality of sensors, wherein each of saidplurality of output values is respectively associated with each of saidplurality of sensors; and (2) an optimizer for determining optimalsetpoint values for the manipulated variables using the model, saidoptimal setpoint values determined by the optimizer in accordance withat least one goal and at least one constraint associated with operationof the power generating unit; and (c) controlling operation of the powergenerating unit using the optimal setpoint values for the manipulatedvariables.

According to yet another aspect of the present invention, there isprovided a control system for controlling operation of a fossil fuelfired power generating unit, said control system comprising: (a) meansfor receiving data signals from a plurality of sensors for sensingparameters associated with operation of the power generating unit; (b) acombustion optimization system for determining optimal setpoint valuesfor manipulated variables associated a combustion process of the fossilfuel fired power generating unit, (c) an oxygen optimization system fordetermining optimal setpoint values for manipulated variables associatedwith air flow for the combustion process; (d) means for controllingoperation of the power generating unit using optimal setpoint values formanipulated variables as determined by the combustion optimizationsystem and the oxygen optimization system.

An advantage of the present invention is the provision of a controlsystem including an oxygen optimization system for optimizing oxygen ina power generating unit.

Another advantage of the present invention is the provision of a controlsystem including an oxygen optimization system that controls air flow tothe boiler.

Still another advantage of the present invention is the provision of acontrol system including an oxygen optimization system that preventsflame instability and flame extinction.

Yet another advantage of the present invention is the provision of acontrol system that includes a combustion optimization system and anoxygen optimization system.

These and other advantages will become apparent from the followingdescription of a preferred embodiment taken together with theaccompanying drawings and the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may take physical form in certain parts and arrangement ofparts, a preferred embodiment of which will be described in detail inthe specification and illustrated in the accompanying drawings whichform a part hereof, and wherein:

FIG. 1 shows a simplified schematic of a typical coal-fired powergenerating unit;

FIG. 2 shows a typical control system for fuel and air in a boiler;

FIG. 3 illustrates the use of manual control of secondary air in thetypical control system for fuel and air in a boiler;

FIG. 4 illustrates an optimization system connected with a distributedcontrol system (DCS) for controlling operation of a power generatingplant;

FIG. 5 illustrates a combustion optimization system;

FIG. 6 illustrates an embodiment of a boiler model used in thecombustion optimization system shown in FIG. 5;

FIG. 7 shows a control system for fuel and air in a boiler, wherein thecontrol system includes an oxygen optimization system according to anembodiment of the present invention;

FIG. 8 illustrates an oxygen optimization system according to anembodiment of the present invention; and

FIG. 9 illustrates an embodiment of an oxygen model used in the oxygenoptimization system shown in FIG. 8.

DETAILED DESCRIPTION OF THE INVENTION

It should be understood that the various systems described in theillustrated embodiments of the present invention may take the form ofcomputer hardware, computer software, or combinations thereof. Thecomputer hardware may take the form of a conventional computer systemincluding a processor, data storage devices, input devices (e.g.,keyboard, mouse, touch screen and the like), and output devices (e.g.,display devices such as monitors and printers), or be embodied as partof another computer system.

Furthermore, the specific inputs and outputs of each model disclosedherein are shown solely for the purpose of illustrating an embodiment ofthe present invention. In this regard, it is contemplated that thespecific model inputs and outputs may vary according to the requirementsof the model and the desired predicted values that are being determinedby the model.

The present invention is described herein with reference to powergenerating units for the generation of electric power. However, it iscontemplated that the present invention is also applicable to otherapplications, including, but not limited to, steam generating units forgeneration of steam.

Power Generating Unit

The main components of a typical fossil fuel fired power generating unit200 will now be briefly described with reference to FIG. 1. Powergenerating unit 200 includes one or more forced draft (FD) fans 210 thatare powered by motors M1. Forced draft fans 210 supply air to mills 214and to burners 222, via an air preheater 212. Ambient air is heated asit passes through air preheater 212. Mills 214 include pulverizers thatare powered by motors M2. The pulverizers grind coal (or other fuel)into small particles (i.e., powder). FD fans 210 provide primary air tomills 214 to dry and carry the coal particles to burners 222 via aprimary air pathway 216. FD fans 210 also provide secondary air toburners 222 via a secondary air pathway 218. Air from FD fans 210 thatis supplied to burners 222 facilitates combustion of the coal. In theillustrated embodiment, air from FD fans 210 also supplies overfire air(OFA) to furnace 224 above the combustion zone, via an overfire air(OFA) pathway 219. The overfire air reduces formation of nitrogen oxides(NOx). Hot flue gas is drawn out of furnace 224 by one or more induceddraft (ID) fans 260, and delivered to the atmosphere though a chimney orstack 290. Induced draft fans 260 are powered by motors M3.

Water is supplied to a drum 226 by control of a feedwater valve 228. Thewater in drum 226 is heated by furnace 224 to produce steam. This steamis further heated in a superheat region 230 by a superheater (notshown). A superheater spray unit (not shown) can introduce a smallamount of water to control the temperature of the superheated steam. Atemperature sensor (not shown) provides a signal indicative of thesensed temperature of the superheated steam. Superheated steam producedby power generating unit 200 is supplied to a turbine 250 that is usedto produce electricity. Steam received by the turbine is reused bycirculating the steam through a reheater (not shown) that reheats thesteam in a reheat region 240. A reheater spray unit (not shown) canintroduce a small amount of water to control the temperature of thereheated steam. A temperature sensor (not shown) provides a signalindicative of the sensed temperature of the reheated steam.

A “boiler” includes, but is not limited to, burners 222, furnace 224,drum 226, superheater, superheater spray unit, reheater, reheater sprayunit, mills 214, and a boiler economizer (not shown). The boilereconomizer recovers “waste heat” from the boiler's hot stack gas andtransfers this heat to the boiler's feedwater.

In addition, power generating unit 200 includes some form ofpost-combustion air pollution control (APC) equipment for removingpollutants from the flue gas. The APC equipment may include, but is notlimited to, a selective catalytic reactor (SCR) 206, an electro-staticprecipitator (ESP) 270, a fabric filter (FF) 272, a spray dry absorber(SDA) 274, and a wet flue gas desulfurization (FGD) system 276.

A typical power generating unit also includes additional components wellknown to those skilled in the art, including, but not limited to, tubesfor carrying fluids, valves, dampers, windbox, sensing devices forsensing a wide variety of system parameters (including, but not limitedto, temperature, pressure, flow rate, and flue gas components), andactuators for actuating components such as valves and dampers.

It should be understood that FIG. 1 illustrates one configuration forsupplying primary air (PA) and secondary air (SA) to burners 222. Inthis regard, forced draft fan 210 is used to supply both the primary airand secondary air to burners 222 via respective primary air andsecondary air pathways 216, 218. The split between the amount of primaryair and secondary air is controlled by a series of dampers and possiblyby an exhauster fan connected to mills 214. It should be appreciatedthat in an alternative configuration commonly used in power generationunits, the supply of primary air and secondary air to the burners iscontrolled by separate fans. In this respect, a primary air fan is usedto control the supply of primary air, while the forced draft fan is usedto control the supply of secondary air.

While the present invention will be described herein with reference topower generating unit 200 as shown in FIG. 1, it is recognized thatdifferent mechanical systems can be used to supply primary and secondaryair. Accordingly, it is contemplated that the present invention may beimplemented in connection with a variety of different types ofmechanical systems for delivery of primary and secondary air in a powergenerating unit.

DCS Control of Primary and Secondary Air

FIG. 2 illustrates a typical control scheme implemented in a DistributedControl System (DCS) for control of fuel, primary air, overfire air, andsecondary air. It should be noted that FIG. 2 is a generalized versionof a control system and a number of variations of the control scheme areimplement on utility boilers. However, FIG. 2 serves herein as arepresentative example of such control schemes.

As shown in FIG. 2, the unit load demand (Unit DMD) and the throttlepressure setpoint of the turbine are used to control the amount of fueland primary air entering the boiler. Unit DMD is input to functioncalculation block, f_(F)(x), that is used to compute the expected amountof fuel needed to maintain the boiler at the Unit DMD. A Proportional,Integral and Derivative (PID) control loop 602 for the throttle pressureis used to trim the fuel demand via addition block 604. The output ofaddition block 604 is the fuel demand (Fuel DMD) for fuel/air system510. Fuel/air system 510 includes the components of power generatingunit 200 that are associated with supplying fuel and air (i.e., oxygen)to the boiler. These components include, but are not limited to suchcomponents as described above in connection with FIG. 1 (e.g., fans,dampers, valves, conduits, etc.).

Fuel DMD is used to compute the primary air demand (PA DMD) usingfunction calculation block, f_(PA)(x). This function calculation blockis used to compute the amount of primary air needed to transport thefuel (i.e., pulverized coal) to the burners.

The overfire air demand (OFA DMD) is computed by summing the output offunction calculation block f_(OFA)(x) and an OFA bias setpoint. Unit DMDis input into function calculation block, f_(OFA)(x), to compute arecommended overfire air (OFA) setpoint. The function, f_(OFA)(x), istypically set during commissioning of the overfire air system ordetermined by engineers during boiler tuning. The operator may bias theresults of the function block using the OFA bias setpoint. In thisrespect, the output of addition block 606 is the overfire air demand(OFA DMD). As illustrated in FIG. 2, only one OFA bias setpoint signalis shown; however, there are often multiple overfire air dampers, andthus multiple OFA bias setpoint signals.

Using the control scheme shown in FIG. 2, the oxygen at the exit of thefurnace is controlled using the secondary air. The expected secondaryair given the unit demand (Unit DMD) is computed using functioncalculation block, f_(SA)(x). The result of this function calculation istrimmed using oxygen PID control loop 610. Accordingly, addition block608 outputs the summation of the outputs of oxygen PID control loop 610and function calculation block f_(SA)(x), which is the secondary airdemand (SA DMD) for fuel/air system 510.

As shown in FIG. 2, fuel/air system 510 is used to deliver the requiredamounts of fuel and air to boiler/steam system 520. Boiler/steam system520 includes components of power generating unit 200 that are used inthe production of steam to drive turbine 250. These components include,but are not limited to, such components described above in connectionwith FIG. 1.

It should be noted that the fuel demand (i.e., Fuel DMD) and air demands(i.e., PA DMD, OFA DMD, and SA DMD) of FIG. 2 may represent multiplesignals. For example, the fuel demand includes signals associated witheach of the mills. In addition, the primary air demand, overfire airdemand, and secondary air demand include multiple signals associatedwith the various dampers and fans that are associated with control ofsystems 510 and 520.

Given the fuel and air demand in FIG. 2, in one embodiment, the fuel/airsystem 510 is implemented as follows: injection of primary air into theboiler is controlled by a primary air fan. The fuel is controlled byfeeder speed in the various mills. The flow of secondary air into theboiler is controlled by forced draft fan and secondary air damperslocated at the entrance to the windbox. Thus, in the illustratedembodiment the primary air demand (PA DMD) is used to set a dischargepressure of the primary air fan; the fuel demand (Fuel DMD) is used toset feeder speeds among various mills; the secondary air demand (SA DMD)is used to set a discharge pressure of the forced draft fan and damperpositions of secondary air dampers; and the overfire air demand (OFADMD) is used to set additional damper positions. PA DMD, OFA DMD and SADMD may be quantitatively expressed as a percentage value indicative ofthe percentage that a valve or other component is opened or actuated.

It should be appreciated that primary air, overfire air and secondaryair can be quantified in terms of a flow rate (e.g., volume per unittime) or a percentage of a maximum flow rate.

The control scheme of FIG. 2 responds to changes in the heat content offuel in the following manner. If the heat content of the fuel decreases,the throttle pressure will also decrease. The output of throttlepressure PID control loop 602 will increase to compensate for the dropin throttle pressure. This results in both an increase in the Fuel DMDand PA DMD signals. The fuel/air system 510 will increase the fuel andprimary air entering the boiler/steam system 520. Using the foregoingapproach, the throttle pressure will be controlled to the desiredsetpoint.

The increase in primary air due to the decrease in heat content of thefuel will cause the oxygen at the exit of the furnace to increase, whichis indicative of an increase in “excess oxygen.” Oxygen PID control loop610 will respond by decreasing the secondary air. Thus, the totaloverall effect of lower heat content in the fuel is a decrease insecondary air which may potentially lead to flame instability orextinction.

A second source of potential flame instability is a drift in themeasured oxygen values provided by oxygen sensors. Oxygen sensors areplaced in the back-pass of the furnace in order to measure theconcentration of oxygen (i.e., “oxygen level” or “amount of oxygen”) inthe flue gas remaining after combustion. Accordingly, the measuredoxygen concentration is indicative of “excess oxygen,” since apercentage value for excess oxygen can be determined from the measuredoxygen concentration. The level of oxygen has a large effect on a numberof important combustion characteristics including the NOx and COemissions, water-wall wastage, and boiler efficiency. Therefore, it iscritical to accurately measure the level of oxygen in the flue gas.

Typically, multiple oxygen sensors are placed in the back-pass of thefurnace to measure the oxygen level at different locations. Theresulting measurements are often combined using simple logic, such asaveraging to form one value for oxygen. This oxygen level measurement isthen used in oxygen PID control loop 610.

Multiple measurements of oxygen are also taken because the oxygensensors are prone to drift and errors, especially if the oxygen sensorsare not properly maintained. By averaging or eliminating an oxygensensor that appears to be inaccurate, the error in the measurement ofoxygen can be reduced.

However, despite multiple oxygen measurements, it is possible thatinaccurate measurements of oxygen are used in oxygen PID control loop610 of FIG. 2. If the oxygen sensors produce a measured oxygen levelthat is higher than the actual oxygen level, oxygen PID control loop 610of FIG. 2 will lower the secondary air demand (SA DMD).

As previously described in the background section, a significant drop insecondary air can significantly increase the probability of flameextinction. At some point as the secondary air is lowered, whether dueto lower heat content in the fuel or an inaccurate reading of oxygen,the probability of flame extinction becomes unacceptably large. Toprevent flame extinction, the operator may choose to increase the oxygensetpoint which results in higher levels of NOx emissions and reductionsin boiler efficiency.

Alternatively, the operator may choose to turn off oxygen PID controlloop 610 and manually control the secondary air demand setpoint, asillustrated by the control scheme shown in FIG. 3. In this case, theoperator uses the second air demand (SA DMD) to manually control theoxygen and prevent flame extinction. The operator uses a variety ofdifferent sensor readings, including potentially the oxygen level,secondary air flow, primary air flow, overfire air damper positions,fuel flow, forced draft fan amps, and flame scanner relative numbers.Using these multiple sources of information, the operator can recognizeconditions caused by low heat content of the fuel or drifting oxygensensors and respond by increasing the secondary air and subsequently theoxygen levels in the flue gas. However, it is difficult for the operatorto maintain manual control on the secondary air at all times, and it isoften easier to simply increase the oxygen setpoint in the controlscheme of FIG. 2 than to maintain manual control of the secondary air asshown by the control scheme of FIG. 3. Moreover, the control scheme ofFIG. 3 cannot be used in conjunction with a combustion optimizationsystem since a critical variable, oxygen, is not under automaticcontrol. The control scheme of FIG. 2 is also not appropriate for usewith a combustion optimization system because the lower limit on theoxygen setpoint is maintained at an artificially high level under mostoperating conditions. The deficiencies in the control schemes of FIGS. 2and 3 are overcome by the present invention by providing an improvedoxygen control system that is based upon optimization.

Optimization System

FIG. 4 illustrates a block diagram of an optimization system 100. In theillustrated embodiment, optimization system 100 is comprised of anoptimizer 110 and a model 120. Optimizer 110 and model 120 are bothdescribed in greater detail below. In accordance with an illustratedembodiment, optimization system 100 may form part of a supervisorycontroller 160 that communicates with a distributed control system (DCS)150. DCS 150 is a computer-based control system that provides regulatorycontrol of a power generating plant 170. DCS 150 may take the form of aprogrammable logic controller (PLC). Supervisory controller 160 is acomputer system that provides supervisory control data to DCS 150. Itshould be understood that in an alternative embodiment, model 120 mayreside on a different computer system than optimizer 110.

An operator interface (not shown) provides means for an operator tocommunicate with DCS 150. DCS 150 may also communicate with a historian(not shown).

Plant 170 includes one or more power generating units 200. Each powergenerating unit 200 includes a plurality of actuators 205 and sensors215. Actuators 205 includes devices for actuating components such asvalves and dampers. Sensors 215 include devices for sensing varioussystem parameters (e.g., temperature, pressure, flow rate, and flue gascomponents).

Model 120 is used to represent the relationship between (a) manipulatedvariables (MV) and disturbance variables (DV) and (b) controlledvariables (CV). Manipulated variables (MVs) may be changed by theoperator or optimization system 100 to affect the controlled variables(CVs). As used herein, disturbance variables refer to variables(associated with power generating unit 200) that affect the controlledvariables, but cannot be manipulated by an operator (e.g., ambientconditions, characteristics of the fuel, characteristics of a fuelblend, characteristics of fuel additives, etc.). Optimizer 110determines an optimal set of setpoint values for the manipulatedvariables given (1) desired goal(s) associated with operation of thepower generating unit (e.g., minimizing NOx production) and (2)constraints associated with operation of the power generating unit(e.g., limits on emissions of NOx, SO₂, CO₂, CO, mercury, ammonia slipand particulate matter).

At a predetermined frequency (e.g., every 10-30 seconds), optimizationsystem 100 obtains the current values of manipulated variables,controlled variables and disturbance variables from DCS 150. An“optimization cycle” commences each time the current values for themanipulated variables, controlled variables and disturbance variablesare read out from DCS 150.

As will be described in further detail below, optimization system 100uses model 120 to determine an optimal set of setpoint values for themanipulated variables based upon current conditions of power generatingunit 200. The optimal set of setpoint values are sent to DCS 150. Anoperator of plant 170 has the option of using the optimal set ofsetpoint values for the manipulated variables. In most cases, theoperator allows the computed optimal set of setpoint values for themanipulated variables to be used as setpoints values for control loops.Optimization system 100 runs in a closed loop adjusting the setpointsvalues of the manipulated variables at a predetermined frequency (e.g.,every 10-30 seconds or more frequently) depending upon current operatingconditions of power generating unit 200.

Neural Network based Dynamic Model

It should be understood that while the present invention is describedherein with reference to models in the form of neural network basedmodels, it is contemplated that the present invention may be implementedusing other types of models, including but not limited to, anempirically developed model, a model based upon heuristics, a supportvector machine (SVM) model, a model developed by linear regression, or amodel developed using “first principles” knowledge. A model that isdeveloped using known physical equations is referred to as a modeldeveloped using “first principles” knowledge.

To properly capture the relationship between the manipulated/disturbancevariables and the controlled variables, model 120 may have the followingcharacteristics:

-   -   Nonlinearity: A nonlinear model is capable of representing a        curve rather than a straight line relationship between        manipulated/disturbance and controlled variables. For example, a        nonlinear, curved relationship is often observed between        overfire air (OFA) dampers and NOx.    -   Multiple Input Multiple Output (MIMO): The model must be capable        of capturing the relationships between multiple inputs        (manipulated/disturbance variables) and multiple outputs        (controlled variables).    -   Dynamic: Changes in the inputs do not instantaneously affect the        outputs. Rather there is a time delay and follow by a dynamic        response to the changes. It may take 15-30 minutes for changes        in the inputs to fully propagate through the system. Since        optimization systems execute at a predetermined frequency (e.g.,        an optimization cycle commencing every 10-30 seconds), the model        must represent the effects of these changes over time and take        them into account.    -   Adaptive: The model must be updated at the beginning of each        optimization cycle (e.g., every 10-30 seconds) to reflect the        current operating conditions of the boiler.    -   Derived from Empirical Data: Since each boiler is unique, the        model must be derived from empirical data obtained from the        power generating unit.

Given the foregoing requirements, a neural network based approach ispresently the preferred technology for implementing models in accordancewith the present invention. Neural networks are developed based uponempirical data using advanced regression algorithms. See, for example,C. Bishop, Neural Networks for Pattern Recognition, Clarendon Press,Oxford, U.K., 1995, fully incorporated herein by reference. Neuralnetworks are capable of capturing the nonlinearity commonly exhibited byboilers. Neural networks can also be used to represent systems withmultiple inputs and outputs. In addition, neural networks can be updatedusing either feedback biasing or on-line adaptive learning.

Dynamic models can also be implemented in a neural network basedstructure. A variety of different types of model architectures have beenused for implementation of dynamic neural networks, as described in S.Piche, “Steepest Descent Algorithms for Neural Network Controllers andFilters,” IEEE Trans. Neural Networks, vol. 5, no. 2, pp. 198-212, 1994and A. Barto, “Connectionist Learning for Control,” Neural Networks forControl, edited by Miller, W., Sutton, R. and Werbos, P., MIT Press, pp5-58, 1990, both of which are fully incorporated herein by reference.Many of the neural network model architectures require a large amount ofdata to successfully train the dynamic neural network. A novel neuralnetwork structure, which may be trained using a relatively small amountof data, was developed in the late 1990's. Complete details on thisdynamic neural network based structure are provided in S. Piche, B.Sayyar-Rodsari, D. Johnson and M. Gerules, “Nonlinear model predictivecontrol using neural networks,” IEEE Control Systems Magazine, vol. 20,no. 2, pp. 53-62, 2000, which is fully incorporated herein by reference.

Given a model of a boiler, it is possible to compute the effects ofchanges in the manipulated variables on the controlled variables.Furthermore, since the model is dynamic, it is possible to compute theeffects of changes in the manipulated variables over a future timehorizon (i.e., multiple changes rather than a single change).

Given that a relationship between inputs and outputs is well representedby the model described above, it will now be described how setpointvalues for inputs (i.e., manipulated variables) can be determined toachieve desired goals while also observing the constraints.

Optimizer

An optimizer is used to minimize a “cost function” subject to a set ofconstraints. The cost function is a mathematical representation of adesired goal or goals. For instance, to minimize NOx, the cost functionincludes a term that decreases as the level of NOx decreases. One commonmethod for minimizing a cost function is known as “gradient descentoptimization.” Gradient descent is an optimization algorithm thatapproaches a local minimum of a function by taking steps proportional tothe negative of the gradient (or the approximate gradient) of thefunction at the current point.

Since the model is dynamic, the effects of changes must be taken intoaccount over a future time horizon. Therefore, the cost functionincludes terms over a future horizon, typically one hour for“combustion” optimization. Since the model is used to predict over atime horizon, this approach is commonly referred to as model predictivecontrol (MPC). Model Predictive Control is described in detail in S.Piche, B. Sayyar-Rodsari, D. Johnson and M. Gerules, “Nonlinear modelpredictive control using neural networks,” IEEE Control SystemsMagazine, vol. 20, no. 2, pp. 53-62, 2000, which is fully incorporatedherein by reference.

Constraints may be placed upon both the inputs (MVs) and outputs (CVs)of the boiler over the future time horizon. Typically, constraints thatare consistent with limits associated with the DCS are placed upon themanipulated variables. Constraints on the outputs (CVs) are determinedby the problem that is being solved.

A nonlinear model can be used to determine the relationship between theinputs and outputs of a boiler. Accordingly, a nonlinear programmingoptimizer is used to solve the optimization problem in accordance withthis embodiment of the present invention. However, it should beunderstood that a number of different optimization techniques may beused depending on the form of the model and the costs and constraints.For example, it is contemplated that the present invention may beimplemented by using, individually or in combination, a variety ofdifferent types of optimization approaches. These optimizationapproaches include, but not limited to, linear programming, quadraticprogramming, mixed integer non-linear programming (NLP), stochasticprogramming, global non-linear programming, genetic algorithms, andparticle/swarm techniques.

Given the cost function and constraints, a non-linear program (NLP)optimizer typically solves problems with 20 manipulated variables and 10controlled variables in less than one second. This is sufficiently fastfor most applications since the optimization cycle is typically in therange of 10-30 seconds. More details on the formulation of the costfunction and constraints are provided in the above mentioned referenceS. Piche, B. Sayyar-Rodsari, D. Johnson and M. Gerules, “Nonlinear modelpredictive control using neural networks,” IEEE Control SystemsMagazine, vol. 20, no. 2, pp. 53-62, 2000, which is fully incorporatedherein by reference.

The optimizer computes the full trajectory of manipulated variable movesover the future time horizon, typically one hour. For an optimizationsystem that executes every 30 seconds, 120 values are computed over aone hour future time horizon for each manipulated variable. Since themodel or goals/constraints may change before the next optimizationcycle, only the first value in the time horizon for each manipulatedvariable is output by the optimization system to the DCS as a setpointvalue for each respective manipulated variable.

At the next optimization cycle, typically 30 seconds later, the model isupdated based upon the current conditions of the boiler. The costfunction and constraints are also updated if they have changed.Typically, the cost function and constraints are not changed. Theoptimizer is used to recompute the set of values for the manipulatedvariables over the time horizon and the first value in the time horizon,for each manipulated variable, is output to the DCS as the setpointvalue for each respective manipulated variable. The optimization systemrepeats this process for each optimization cycle (e.g., every 30second), thus, constantly maintaining optimal performance as the boileris affected by changes in such items as load, ambient conditions, boilerconditions, and fuel characteristics.

Combustion Optimization System

An oxygen optimization system according to the present invention is usedto overcome the problems described in connection with the controlschemes of FIGS. 2 and 3. The oxygen optimization system will bedescribed in detail below. However, a combustion optimization systemwill first be described in order to support the importance of the oxygenoptimization system.

FIG. 5 shows a combustion optimization system 300. Combustionoptimization system 300 communicates with a distributed control system(DCS) 150 to control a boiler to achieve the desired combustioncharacteristics, based upon constraints and goals specified by anoperator or an engineer. Combustion optimization system 300 includes aboiler model 320 and an optimizer 310. Boiler 370 includes thecomponents discussed above, as well as actuators 372 and sensors 374.DCS 150 communicates current values of the manipulated, disturbance, andcontrolled variables to combustion optimization system 300.

Combustion optimization system 300 uses model 320, optimizer 310, goalsand constraints as described above. FIG. 6 shows an embodiment of model320 used in combustion optimization system 300.

By way of example, and not limitation, the manipulated variable (MV)inputs to model 320 may include the following: level of excess oxygen inthe flue gas, the overfire air (OFA) damper positions, thewindbox-to-furnace differential pressure (WFDP), biases to each of themills, and the burner tilt angles. The disturbance variable (DV) inputsto model 320 may typically include the following: fuel characteristics,fineness of the mill grind, and load demand. The above-identifiedmanipulated variables and disturbance variables for illustrated model320 will now be briefly described.

“Excess oxygen” refers to the percentage amount of excess oxygenintroduced into the furnace above that required for full combustion ofthe fuel. As the amount of excess oxygen increases, the boiler operatesin an air rich environment.

With respect to “overfire air (OFA) damper positions,” overfire air isintroduced above the combustion zone in a furnace in order to reduce COemissions and lower NOx emissions. The amount of overfire air iscontrolled by the position of a damper.

The “windbox to furnace differential pressure (WFDP)” controls thevelocity of secondary air entry into the boiler which affects thelocation of the combustion within the furnace.

With respect to “mill bias,” mills are used to grind the coal before theprimary air transports the coal dust into the furnace through theburner. The amount of coal ground by each mill is determined primarilyby load. However, it is possible to bias the amount of coal such thatmore or less coal is introduced at various levels. This can be used toincrementally affect the staging of combustion.

As to “coal characteristic,” the chemical composition of coal changeseven if it is extracted from the same mine. Changes in nitrogen, sulfur,mercury and BTU content are common.

With respect to “mill grind,” as described above, mills are used togrind the coal into a fine dust that can be injected into a furnace. Thefineness of the grind changes over time as the mill wears.

The term “load” refers to the required electrical power generation ratefor a power generating unit.

Model 320 is used to predict the effects of changes in the manipulatedand disturbance variables on the outputs of the boiler. FIG. 6illustrates one embodiment of the potential set of controlled variable(CV) outputs of model 320. In this embodiment, model 320 is used topredict emissions from the power generating unit (i.e., mercury,nitrogen oxides, and carbon monoxide), the amount of carbon in the ash(CIA), boiler efficiency, and steam temperatures (i.e., main, superheatand reheat temperatures).

Optimizer 310 uses model 320 of FIG. 6, along with the goals andconstraints, in order to determine optimal combustion. The goals areexpressed in the form of a cost function. In one embodiment, the costfunction may be used to minimize the amount of emissions (such as NOxand mercury), while observing constraints on variables such as CO, CIAor both CO and CIA. In addition, the cost function may also be used tomake trade-offs between boiler efficiency and emissions. Also, the costfunction may be used to maintain steam temperatures at desiredset-points. Finally, boiler and environmental consideration may placeadditional constraints upon the manipulated variables, such as a lowerlimit on the allowed excess oxygen. Using the foregoing approach,combustion optimization system 300 can be used to determine the optimalsetpoint values of manipulated variables, based upon current operatingconditions and the desires of operators and engineers.

In addition to the embodiment described above, U.S. patent applicationSer. No. 10/985,705 (filed Nov. 10, 2004), entitled “System forOptimizing a Combustion Heating Process” (fully incorporated herein byreference) discloses a combustion optimization approach to modelingcontrollable losses in a power generating unit, and a method foroptimizing the combustion process based upon these controllable losses.Also, optimization of sootblowing can be included in a combustionoptimization as described in the U.S. patent application Ser. No.11/053,734 (filed Feb. 8, 2005), entitled “Method and Apparatus forOptimizing Operation of a Power Generation Plant Using ArtificialIntelligence Techniques” (fully incorporated herein by reference).Furthermore, U.S. patent application Ser. No. 11/301,034 (filed Dec. 12,2005), entitled “Model Based Control and Estimation of MercuryEmissions” (fully incorporated herein by reference) discloses acombustion optimization system and a method for reducing mercuryemissions from a coal-fired power plant, while observing limits on theamount of carbon in the fly ash produced by the combustion process.

Oxygen Optimization System

Referring now to FIG. 7, there is shown a control scheme according to anembodiment of the present invention that includes an oxygen optimizationsystem 400. The control scheme of FIG. 7 is a modification of thecontrol scheme of FIG. 2. In this respect, oxygen PID control loop 610of FIG. 2 has been replaced by oxygen optimization system 400. Fueldemand (Fuel DMD) is controlled by throttle pressure PID control loop602 in the same manner as described above in connection with the controlscheme of FIG. 2. However, unlike the control scheme of FIG. 2, oxygenoptimization system 400 is used to determine primary air demand (PADMD), overfire air demand (OFA DMD), and secondary air demand (SA DMD).PA DMD, OFA DMD, and SA DMD are manipulated variables (MVs) of oxygenoptimization system 400.

Oxygen optimization system 400 is used to control secondary air flow(i.e., “secondary air”) and oxygen, which are controlled variables(CVs). The unit demand (Unit DMD) and fuel demand (Fuel DMD) aredisturbance variables (DVs) in oxygen optimization system 400. Unitdemand (Unit DMD) may be directly input into oxygen optimization system400, or alternatively, calculations associated with function calculationblocks for primary air (PA_(calc)), overfire air (OFA_(calc)), andsecondary air (SA_(calc)) may be input to oxygen optimization system, asshown in FIG. 7. Furthermore, the desired setpoint value for oxygen isinput to oxygen optimization system 400.

FIG. 8 shows a detailed view of oxygen optimization system 400. Oxygenoptimization system 400 communicates with a DCS 150 to control air flowto the boiler to achieve the desired combustion characteristics, basedupon specified goals and constraints. Oxygen optimization system 400includes a model 420 and an optimizer 410. As indicated above inconnection with FIG. 5, boiler 370 includes actuators 372 and sensors374, as well as the components discussed in detail above. DCS 150communicates current values of manipulated variables (MVs), disturbancevariable (DVs), and controlled variables (CVs) to oxygen optimizationsystem 400.

Oxygen optimization system 400 uses model 420, optimizer 410, as well asgoals and constraints, as described above. FIG. 9 illustrates anembodiment of model 420. Model 420 is used to predict the effects ofchanges in manipulated variables (MVs) and disturbance variables (DVs)on the controlled variable (CV) outputs associated with fuel/air system510 and boiler/steam system 520. In the illustrated embodiment, anoutput of fuel/air system 510 is the secondary air flow (i.e.,“secondary air”), and an output of the boiler/steam system 520 is oxygenin the boiler. Accordingly, secondary air and oxygen are predictedcontrolled variable (CV) outputs of model 420. It should be appreciatedthat the controlled variables (CV) shown in FIG. 9 are exemplary, andare not intended to limit the scope of the present invention.

By way of example, and not limitation, the manipulated variable (MV)inputs to model 420 may include one or more of the following: primaryair demand (PA DMD), secondary air demand (SA DMD), and overfire airdemand (OFA DMD). The disturbance variable (DV) inputs to model 420 maytypically include one or more of the following: unit load demand (UnitDMD) or alternatively, associated function calculation block values ofthe unit load demand (i.e., PA_(calc), OFA_(calc), and SA_(calc)), andfuel demand (Fuel DMD).

It should be understood that the manipulated variables, disturbancevariables and controlled variables described in connection with oxygenoptimization system 400 are exemplary only, and that other manipulatedvariables, disturbance variables and controlled variables may be used inconnection with the present invention.

Optimizer 410 uses model 420 of FIG. 9, along with the goals andconstraints (e.g., limits on secondary air, limit on oxygen, limit onemissions of CO, and limit on emissions of NOx), in order to determineoptimal air flow to the boiler. The goals are expressed in amathematical form as a cost function. Optimizer 410 is used to minimize(or maximize) the cost function subject to the constraints. Theconstraints associated with the cost function may include both hardconstraints (i.e., constraints that must be necessarily satisfied) andsoft constraints (i.e., constraints that express a preference for somesolutions over other solutions). Violations of hard constraints may havea relatively high penalty, whereas violations of soft constraints canhave varying “regions of penalties.” One common method for minimizing acost function is known as “gradient descent optimization.” Gradientdescent is an optimization algorithm that approaches a local minimum ofa function by taking steps proportional to the negative of the gradient(or the approximate gradient) of the function at the current point.

In one embodiment of the present invention, upper and lower softconstraints implemented in the cost function may be placed upon theoxygen, a controlled variable (CV). In addition, a desired setpointvalue for the oxygen may be placed at or below the lower softconstraint. Finally, upper and lower soft constraints of greaterpriority are placed upon the secondary air flow, a controlled variable(CV).

For example, at full load, an upper soft constraint may be placed on theoxygen at 5% (i.e., an oxygen concentration of 5% in the flue gas) whilea lower soft constraint may be place at 3%. A weighting factor of 10 maybe placed upon these soft constraints. A desired setpoint value of 2.5%may be place on the oxygen with a weighting factor of 1. Since the softconstraints have a higher weighting factor than the desired setpointvalue, optimizer 410 will try to move oxygen towards 2.5% but will notmove it much below 3%, due to the significant penalty caused by thehigher weighting factor of the soft constraints.

An upper soft constraint may be placed on secondary air at 90% (i.e., aflow rate or velocity of secondary air that is 90% of a maximum flowrate or velocity) while a lower soft constraint may be placed onsecondary air at 80%. A weighting factor of 100 may be placed on thesesoft constraints. Because a larger weighting factor is used on the softconstraints on secondary air than on the soft constraints on oxygen, thesoft constraints on secondary air will override the soft constraints onoxygen.

Using this approach, the lower soft constraint and desired setpointvalue of oxygen are set based upon operational and environment concernsrather than flame stability concerns. Under normal conditions, oxygenoptimization system 400 will facilitate operation at the lower softconstraint of oxygen, near the desired setpoint value of oxygen.However, if the heat content of the fuel decreases, more primary air isneeded, thereby decreasing the flow of secondary air. If the secondaryair hits its lower soft constraint, the oxygen optimization system 400can shift air from the overfire air system to the secondary air system.If there is not sufficient air available in the overfire air system, theoxygen optimization system 400 will automatically allow the oxygen toincrease due to the higher priority soft constraint upon secondary air.If allowed, optimization system 400 may also reduce primary air demandin this situation. The amount of allowable reduction in primary airdemand is bounded by hard constraints. Typically, the amount ofallowable reduction in primary air demand is small due to the need toprovide sufficient air to convey fuel to the burners. Using theforegoing approach, the oxygen level is minimized while guaranteeingthat the needed secondary air and flame stability are maintained in theboiler.

The lower soft constraint upon the secondary air also counteracts theeffects of an artificially high oxygen reading. As described above, anartificial high reading of oxygen causes the secondary air to decreasein order to maintain the desired level of oxygen. However, as thesecondary air decreases, it will become constrained in oxygenoptimization system 400. As a result, the negative effect of aninaccurate oxygen measurement is counteracted.

Similarly, the upper soft constraint upon secondary air is used tocounteract the effects of an artificially low reading of oxygen. In thiscase, the artificially low reading of oxygen causes the secondary air toincrease. By constraining the secondary air in oxygen optimizationsystem 400, the effects of an artificial low reading of oxygen can beminimized.

It should be understood that constraints on secondary air and oxygen maychange as a factor of load demand.

Multi-Oxygen Optimization System

In the embodiment described above, oxygen optimization system 400 isused to control a single oxygen level associated with the boiler/steamsystem 520. The measured oxygen level associated with the boiler/steamsystem 520 may be obtained from a single oxygen sensor or from multipleoxygen sensors located in different regions of the boiler/steam system520. In the case of multiple oxygen sensors, oxygen level readings areobtained from each of the oxygen sensors and combined into a singlemeasured oxygen value. For example, readings from each of the oxygensensors may be averaged to determined a single measured oxygen level.The single measured oxygen level is input to oxygen optimization system400.

According to an alternative embodiment of the present invention, oxygenoptimization system 400 is used to control a plurality of oxygen levelsrespectively associated with a plurality of oxygen sensors (i.e., a gridof oxygen sensors). Each of the oxygen sensors may be located in adifferent region of the boiler/steam system 520.

In the control scheme of FIG. 2, it is necessary to combine themeasurements from the control system into a single oxygen value that isused in PID control loop 610. Since oxygen optimization system 400 ofFIG. 7 is capable of controlling multiple outputs, it is not necessaryto combine multiple oxygen level readings into a single measured oxygenvalue. Instead, the oxygen levels respectively associated with the gridof oxygen sensors may be individually controlled or optionallycontrolled in a grouped manner. In this regard, the manipulatedvariables (MVs) and disturbance variables (DVs) input to oxygen model420 are the same as those described in the previous embodiment. However,the controlled variables (CVs) output by oxygen model 420 are secondaryair and a plurality of oxygen levels respectively associated with thegrid of oxygen sensors (i.e., oxygen₁, oxygen₂, oxygen₃, etc). Thepreviously described soft constraints and desired setpoint values areused on the controlled variables (CVs), i.e., the secondary air and theplurality of oxygen levels. The resulting optimization scheme attemptsto reduce each measured oxygen level (associated with the grid of oxygensensors) to the minimum soft constraint of the oxygen CVs. Once again,changes in heat content are handled by shifting overfire air oralternatively allowing the oxygen to increase to prevent secondary airfalling below the soft constraint of the secondary air CV.

The upper soft constraint on oxygen is used to counteract the effects oflarge disparities among the plurality of oxygen sensor readings. Forexample, in a system with two oxygen sensors, if the first oxygen sensormeasures 5.5% and the second oxygen sensor measures 3%, without an uppersoft constraint on oxygen, optimization system 400 would not adjustoxygen. However, with an upper soft constraint on oxygen of 5%,optimization system 400 will lower the overall oxygen, such that thefirst oxygen sensor measures 2.75% while the second oxygen sensormeasures 5.25%. By using an upper soft constraint on oxygen, largedisparities in oxygen sensor readings can be addressed using oxygenoptimization system 400 of FIG. 7.

It should be appreciated that an optimization system may also be used inthe manner described above to control other parameters of the powergenerating unit (e.g., NOx emissions, CO emissions, CO₂ emissions,mercury emissions, ammonia, H₂O, or temperature). In this regard, aplurality of sensing devices for sensing a parameter of the powergenerating unit (e.g., NOx sensors, CO sensors, CO₂ sensors, mercurysensors, ammonia sensors, H₂O sensors, or temperature sensors) arespatially located in different regions of the power generating unit in amanner similar to the oxygen sensors described above. Accordingly, anoptimization system can be used to control a plurality of parameterlevels at different regions of the power generating unit. The parametersare associated with controlled variables of the power generating unit.

Combined Combustion Optimization and Oxygen Optimization Systems

The previously described combustion optimization system 300 and oxygenoptimization system 400 may be combined to optimize operation of aboiler. In this respect, the two optimization systems 300, 400 operatein a master-slave configuration, wherein combustion optimization system300 serves as the master and oxygen optimization system 400 serves asthe slave.

In one embodiment, combustion optimization system 300 is used todetermine optimal setpoint values for manipulated variables (MVs)including “excess oxygen.” The optimal setpoint value for excess oxygenis then used for the desired setpoint value for oxygen in oxygenoptimization system 400. The manipulated variable (MV) constraints onexcess oxygen in combustion optimizer 310 and the controlled variable(CV) soft constraints on oxygen in oxygen optimizer 410 are set equal toeach other to provide consistency between the optimization systems 300and 400.

Using the foregoing approach, NOx emissions can be minimized, boilerefficiency can maximized, and flame stability can be automaticallymaintained even in the presence of large variations in heat content ofthe fuel or in the case of large drift in sensor readings of oxygen inthe boiler.

In another embodiment, an “oxygen profile” obtained from multiple,spatially separated oxygen sensors in a boiler and the overall averageoxygen are controlled using a combustion optimization system. Theoverall average oxygen may be controlled by a DCS PID control loop thatadjusts the total air flow to the boiler as shown in FIG. 2, or as partof a combined combustion optimization and oxygen optimization system.The oxygen profile is controlled by determining the deviation of eachindividual oxygen sensor from the overall average oxygen valuedetermined from a grid of oxygen sensors. These individual deviationsare then used as controlled variables (CVs) in the combustionoptimization system. In this embodiment, the manipulated variables (MVs)are the secondary air and overfire air dampers (i.e., SA DMD and OFADMD), which are used to shift the distribution of air in the boiler toinfluence individual oxygen sensors in the grid. Controlling the oxygenprofile allows the combustion optimization system to better addressindividual operating regions within the boiler.

Other modifications and alterations will occur to others upon theirreading and understanding of the specification. It is intended that allsuch modifications and alterations be included insofar as they comewithin the scope of the invention as claimed or the equivalents thereof.

1. A control system for controlling a parameter associated withoperation of a fossil fuel fired power generating unit, said controlsystem comprising: means for receiving data signals from a plurality ofsensors for sensing the parameter associated with operation of the powergenerating unit, said plurality of sensors located in different regionsof the power generating unit; an optimization system comprising: (a) amodel for predicting the parameter sensed by the plurality of sensors,the model comprising: a plurality of inputs for receiving input valuesassociated with manipulated variables and disturbance variablesassociated with the power generating unit, and a plurality of outputsfor providing a plurality of output values associated with controlledvariables, said plurality of output values indicative of predictedvalues for the parameter sensed by the plurality of sensors, whereineach of said plurality of output values is respectively associated witheach of said plurality of sensors; (b) an optimizer for determiningoptimal setpoint values for each of the manipulated variables byaccessing said model to minimize a cost value of a cost function whileobserving at least one constraint associated with operation of the powergenerating unit, said cost value affected by the value for each saidmanipulated variable, wherein said optimizer determines the optimalsetpoint values in accordance with at least one goal and the at leastone constraint associated with operation of the power generating unit;and means for controlling operation of the power generating unit usingthe optimal setpoint values for the manipulated variables.
 2. A controlsystem according to claim 1, wherein said model is selected from thegroup consisting of the following: a neural network model, anempirically developed model, a model developed using “first principles,”a support vector machine (SVM) model, a model developed by linearregression and a model based upon heuristics.
 3. A control systemaccording to claim 1, wherein said optimizer is selected from the groupconsisting of the following: linear programming, quadratic programming,mixed integer non-linear programming (NLP), stochastic programming,global non-linear programming, genetic algorithms, and particle/swarmtechniques.
 4. A control system according to claim 1, wherein said costvalue is affected by the value of each said manipulated variable over aplurality of time intervals, said optimizer determining a respectivevalue for each said manipulated variable for the plurality of timeintervals in accordance with minimization of said cost value.
 5. Acontrol system according to claim 4, wherein the respective value ofeach said manipulated variable for a first time interval of theplurality of time intervals is determined as said respective optimalvalue for an optimization cycle.
 6. A control system according to claim4, wherein said optimizer determines said respective value for each saidmanipulated variable for said plurality of time intervals whileobserving said at least one constraint across said plurality of timeintervals.
 7. A control system according to claim 1, wherein said costfunction and said at least one constraint are determined to preventflame extinction and flame instability in the boiler of the powergenerating unit.
 8. A control system according to claim 1, wherein saidcost function and said at least one constraint are determined tocounteract the effects of variations in the fossil fuel.
 9. A controlsystem according to claim 1, wherein said cost function and said atleast one constraint are determined to counteract effects of drift indata signals indicative of oxygen concentration generated by oxygensensors.
 10. A system according to claim 1, wherein said at least oneconstraint includes at least one of the following: a limit on secondaryair, a limit on oxygen, a limit on emissions of CO, and a limit onemissions of NOx.
 11. A control system according to claim 1, whereinsaid manipulated variables include at least one of the following:primary air demand, secondary air demand, overfire air demand, forcedair fan, primary air fan, forced air fan, induced air fan, exhausterfan, and mill bias; said disturbance variables include at least one ofthe following: fuel demand, unit demand, characteristics of fuel blend,characteristics of each fuel additive, and associated calculations ofunit demand; and said controlled variables include at least one of thefollowing: oxygen, secondary air, nitrogen oxide emissions, carbonmonoxide emissions, and boiler efficiency.
 12. A control systemaccording to claim 1, wherein said parameter sensed by the plurality ofsensors is oxygen in flue gas; and the optimization system is an oxygenoptimization system.
 13. A control system according to claim 1, whereinsaid parameter sensed by the plurality of sensors is selected from thegroup consisting of: mercury, carbon in ash, nitrogen oxides, and carbonmonoxide; and the optimization system is a combustion optimizationsystem.
 14. A control system according to claim 1, wherein saidparameter sensed by said plurality of sensors is selected from the groupconsisting of: NOx, oxygen, CO, CO₂, H₂O, temperature, ammonia, andmercury.
 15. A method for controlling a parameter associated withoperation of a fossil fuel fired power generating unit, said methodcomprising: receiving data signals from a plurality of sensors forsensing the parameter associated with operation of the power generatingunit, said plurality of sensors located in different regions of thepower generating unit; optimizing operation of the fossil fuel firedpower generating unit using an optimization system comprising: (a) amodel for predicting the parameter sensed by the plurality of sensors,the model comprising: a plurality of inputs for receiving input valuesassociated with manipulated variables and disturbance variablesassociated with the power generating unit, and a plurality of outputsfor providing a plurality of output values associated with controlledvariables, said plurality of output values indicative of predictedvalues for the parameter sensed by the plurality of sensors, whereineach of said plurality of output values is respectively associated witheach of said plurality of sensors; and (b) an optimizer for determiningoptimal setpoint values for each of the manipulated variables byaccessing said model to minimize a cost value of a cost function whileobserving said at least one constraint associated with operation of thepower generating unit, said cost value affected by the value for eachsaid manipulated variable, wherein said optimizer determines the optimalsetpoint values in accordance with at least one goal and the at leastone constraint associated with operation of the power generating unit;and controlling operation of the power generating unit using the optimalsetpoint values for the manipulated variables.
 16. A method according toclaim 15, wherein said model is selected from the group consisting ofthe following: a neural network model, an empirically developed model, amodel developed using “first principles,” a support vector machine (SVM)model, a model developed by linear regression and a model based uponheuristics.
 17. A method according to claim 15, wherein said optimizeris selected from the group consisting of the following: linearprogramming, quadratic programming, mixed integer non-linear programming(NLP), stochastic programming, global non-linear programming, geneticalgorithms, and particle/swarm techniques.
 18. A method according toclaim 15, wherein said cost value is affected by the value of each saidmanipulated variable over a plurality of time intervals, said optimizerdetermining a respective value for each said manipulated variable forthe plurality of time intervals in accordance with minimization of saidcost value.
 19. A method according to claim 18, wherein the respectivevalue of each said manipulated variable for a first time interval of theplurality of time intervals is determined as said respective optimalvalue for an optimization cycle.
 20. A method according to claim 18,wherein said optimizer determines said respective value for each saidmanipulated variable for said plurality of time intervals whileobserving said at least one constraint across said plurality of timeintervals.
 21. A method according to claim 15, wherein said costfunction and said at least one constraint are determined to preventflame extinction and flame instability in the boiler of the powergenerating unit.
 22. A method according to claim 15, wherein said costfunction and said at least one constraint are determined to counteractthe effects of variations in the fossil fuel.
 23. A method according toclaim 15, wherein said cost function and said at least one constraintare determined to counteract effects of drift in data signals indicativeof oxygen concentration generated by oxygen sensors.
 24. A systemaccording to claim 15, wherein said at least one constraint include atleast one of the following: a limit on secondary air, a limit on oxygen,a limit on emissions of CO, and a limit on emissions of NOx.
 25. Amethod according to claim 15, wherein said manipulated variables includeat least one of the following: primary air demand, secondary air demand,overfire air demand, forced air fan, primary air fan, forced air fan,induced air fan, exhauster fan, and mill bias; said disturbancevariables include at least one of the following: fuel demand, unitdemand, characteristics of fuel blend, characteristics of each fueladditive, and associated calculations of unit demand; and saidcontrolled variables include at least one of the following: oxygen,secondary air, nitrogen oxide emissions, carbon monoxide emissions, andboiler efficiency.
 26. A method according to claim 15, wherein saidparameter sensed by the plurality of sensors is oxygen in flue gas; andthe optimization system is an oxygen optimization system.
 27. A methodaccording to claim 15, wherein said parameter sensed by the plurality ofsensors is selected from the group consisting of: mercury, carbon inash, nitrogen oxides, and carbon monoxide; and the optimization systemis a combustion optimization system.
 28. A method according to claim 15,wherein said parameter sensed by said plurality of sensors is selectedfrom the group consisting of: NOx, oxygen, CO, CO₂, H₂O, temperature,ammonia, and mercury.
 29. A control system for controlling operation ofa fossil fuel fired power generating unit, said control systemcomprising: means for receiving data signals from a plurality of sensorsfor sensing parameters associated with operation of the power generatingunit; a combustion optimization system for determining optimal setpointvalues for manipulated variables associated a combustion process of thefossil fuel fired power generating unit; an oxygen optimization systemfor determining optimal setpoint values for manipulated variablesassociated with air flow for the combustion process, wherein saidcombustion optimization system and said oxygen optimization system eachinclude: (a) a model for predicting values of controlled variablesassociated with operation of the power generating unit, the modelcomprising: a plurality of inputs for receiving input values associatedwith manipulated variables and disturbance variables of the powergenerating unit, and a plurality of outputs for providing a plurality ofoutput values indicative of predicted values for the controlledvariables; and (b) an optimizer for determining the optimal setpointvalues for each of the manipulated variables by accessing said model tominimize a cost value of a cost function while observing at least oneconstraint associated with operation of the power generating unit, saidcost value affected by the value for each said manipulated variable,wherein said optimizer determines the optimal setpoint values inaccordance with at least one goal and the at least one constraintassociated with operation of the power generating unit; and means forcontrolling operation of the power generating unit using optimalsetpoint values for manipulated variables as determined by thecombustion optimization system and the oxygen optimization system.
 30. Acontrol system according to claim 29, wherein one of the optimalsetpoint values determined by the combustion optimization system is avalue for excess oxygen, said value for excess oxygen used as a desiredsetpoint value for oxygen in the oxygen optimization system, whereinoxygen is a controlled variable in the oxygen optimization system.